{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.0.0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras = tf.keras\n",
    "layers = tf.keras.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_path = glob.glob('./dc/train/*/*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./dc/train\\\\dog\\\\dog.995.jpg',\n",
       " './dc/train\\\\dog\\\\dog.996.jpg',\n",
       " './dc/train\\\\dog\\\\dog.997.jpg',\n",
       " './dc/train\\\\dog\\\\dog.998.jpg',\n",
       " './dc/train\\\\dog\\\\dog.999.jpg']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_path[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_label = [int(p.split('\\\\')[1] == 'cat') for p in train_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_label[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_label[ :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_preprosess_image(path, label):\n",
    "    image = tf.io.read_file(path)\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    image = tf.image.resize(image, [256, 256])\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = image/255\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = tf.data.Dataset.from_tensor_slices((train_image_path, train_image_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = train_image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParallelMapDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img, label in train_image_ds.take(2):\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "train_count = len(train_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = train_image_ds.shuffle(train_count).repeat().batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_path = glob.glob('./dc/test/*/*.jpg')\n",
    "test_image_label = [int(p.split('\\\\')[1] == 'cat') for p in test_image_path]\n",
    "test_image_ds = tf.data.Dataset.from_tensor_slices((test_image_path, test_image_label))\n",
    "test_image_ds = test_image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)\n",
    "test_image_ds = test_image_ds.repeat().batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_count = len(test_image_path)\n",
    "test_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "keras内置经典网络实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The input must have 3 channels; got `input_shape=(150, 150, 1)`",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-b70f86adb7e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m covn_base = tf.keras.applications.VGG16(weights='imagenet', \n\u001b[0;32m      2\u001b[0m                                         \u001b[0minclude_top\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m                                         input_shape=(150, 150, 1))\n\u001b[0m",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\applications\\__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     47\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'models'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'utils'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mbase_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\applications\\vgg16.py\u001b[0m in \u001b[0;36mVGG16\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mkeras_modules_injection\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mVGG16\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mvgg16\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVGG16\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras_applications\\vgg16.py\u001b[0m in \u001b[0;36mVGG16\u001b[1;34m(include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)\u001b[0m\n\u001b[0;32m     97\u001b[0m                                       \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimage_data_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m                                       \u001b[0mrequire_flatten\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m                                       weights=weights)\n\u001b[0m\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras_applications\\imagenet_utils.py\u001b[0m in \u001b[0;36m_obtain_input_shape\u001b[1;34m(input_shape, default_size, min_size, data_format, require_flatten, weights)\u001b[0m\n\u001b[0;32m    314\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'imagenet'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m                     raise ValueError('The input must have 3 channels; got '\n\u001b[1;32m--> 316\u001b[1;33m                                      '`input_shape=' + str(input_shape) + '`')\n\u001b[0m\u001b[0;32m    317\u001b[0m                 if ((input_shape[0] is not None and input_shape[0] < min_size) or\n\u001b[0;32m    318\u001b[0m                    (input_shape[1] is not None and input_shape[1] < min_size)):\n",
      "\u001b[1;31mValueError\u001b[0m: The input must have 3 channels; got `input_shape=(150, 150, 1)`"
     ]
    }
   ],
   "source": [
    "covn_base = tf.keras.applications.VGG16(weights='imagenet', \n",
    "                                        include_top=False, \n",
    "                                        input_shape=(150, 150, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.image.grayscale_to_rgb?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "covn_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "covn_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(covn_base)\n",
    "model.add(layers.GlobalAveragePooling2D())\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, None, None, 512)   14714688  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 14,977,857\n",
      "Trainable params: 263,169\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, None, None, 512)   14714688  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 14,977,857\n",
      "Trainable params: 263,169\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(lr=0.0005),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 62 steps, validate for 31 steps\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0914 11:34:31.748271  7848 deprecation.py:323] From c:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62/62 [==============================] - 118s 2s/step - loss: 0.5895 - acc: 0.7077 - val_loss: 0.4528 - val_acc: 0.8528\n",
      "Epoch 2/15\n",
      "62/62 [==============================] - 114s 2s/step - loss: 0.4062 - acc: 0.8584 - val_loss: 0.3572 - val_acc: 0.8639\n",
      "Epoch 3/15\n",
      "62/62 [==============================] - 126s 2s/step - loss: 0.3332 - acc: 0.8695 - val_loss: 0.2861 - val_acc: 0.8942\n",
      "Epoch 4/15\n",
      "62/62 [==============================] - 125s 2s/step - loss: 0.2832 - acc: 0.8957 - val_loss: 0.2543 - val_acc: 0.9042\n",
      "Epoch 5/15\n",
      "62/62 [==============================] - 125s 2s/step - loss: 0.2544 - acc: 0.9022 - val_loss: 0.2741 - val_acc: 0.8841\n",
      "Epoch 6/15\n",
      "62/62 [==============================] - 127s 2s/step - loss: 0.2374 - acc: 0.9118 - val_loss: 0.2230 - val_acc: 0.9153\n",
      "Epoch 7/15\n",
      "62/62 [==============================] - 126s 2s/step - loss: 0.2170 - acc: 0.9158 - val_loss: 0.2410 - val_acc: 0.8942\n",
      "Epoch 8/15\n",
      "62/62 [==============================] - 125s 2s/step - loss: 0.2076 - acc: 0.9254 - val_loss: 0.2198 - val_acc: 0.9103\n",
      "Epoch 9/15\n",
      "62/62 [==============================] - 126s 2s/step - loss: 0.1975 - acc: 0.9279 - val_loss: 0.2014 - val_acc: 0.9304\n",
      "Epoch 10/15\n",
      "62/62 [==============================] - 125s 2s/step - loss: 0.1777 - acc: 0.9385 - val_loss: 0.2043 - val_acc: 0.9204\n",
      "Epoch 11/15\n",
      "62/62 [==============================] - 123s 2s/step - loss: 0.1837 - acc: 0.9259 - val_loss: 0.2256 - val_acc: 0.8982\n",
      "Epoch 12/15\n",
      "62/62 [==============================] - 123s 2s/step - loss: 0.1744 - acc: 0.9385 - val_loss: 0.1956 - val_acc: 0.9254\n",
      "Epoch 13/15\n",
      " 6/62 [=>............................] - ETA: 1:12 - loss: 0.1392 - acc: 0.9563"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-c11be38c9f6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_image_ds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     validation_steps=test_count//BATCH_SIZE)\n\u001b[0m",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    732\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 734\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    122\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 86\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    413\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 415\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    416\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m       \u001b[1;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1820\u001b[0m     \u001b[1;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1821\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1822\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1824\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1141\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1222\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m   1225\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    512\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_image_ds,\n",
    "    steps_per_epoch=train_count//BATCH_SIZE,\n",
    "    epochs=15,\n",
    "    validation_data=test_image_ds,\n",
    "    validation_steps=test_count//BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "covn_base.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covn_base.layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_at = -3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in covn_base.layers[:fine_tune_at]:\n",
    "    layer.trainable =  False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer = tf.keras.optimizers.Adam(lr=0.0005/10),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 62 steps, validate for 31 steps\n",
      "Epoch 13/22\n",
      "62/62 [==============================] - 115s 2s/step - loss: 0.2120 - accuracy: 0.9083 - val_loss: 0.1539 - val_accuracy: 0.9325\n",
      "Epoch 14/22\n",
      "62/62 [==============================] - 119s 2s/step - loss: 0.0860 - accuracy: 0.9743 - val_loss: 0.1330 - val_accuracy: 0.9405\n",
      "Epoch 15/22\n",
      "62/62 [==============================] - 127s 2s/step - loss: 0.0516 - accuracy: 0.9854 - val_loss: 0.1450 - val_accuracy: 0.9415\n",
      "Epoch 16/22\n",
      "62/62 [==============================] - 129s 2s/step - loss: 0.0343 - accuracy: 0.9904 - val_loss: 0.1345 - val_accuracy: 0.9526\n",
      "Epoch 17/22\n",
      "62/62 [==============================] - 132s 2s/step - loss: 0.0139 - accuracy: 0.9990 - val_loss: 0.1379 - val_accuracy: 0.9546\n",
      "Epoch 18/22\n",
      "62/62 [==============================] - 130s 2s/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.1352 - val_accuracy: 0.9556\n",
      "Epoch 19/22\n",
      "62/62 [==============================] - 132s 2s/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.1400 - val_accuracy: 0.9516\n",
      "Epoch 20/22\n",
      "62/62 [==============================] - 131s 2s/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.1550 - val_accuracy: 0.9415\n",
      "Epoch 21/22\n",
      "18/62 [=======>......................] - ETA: 1:14 - loss: 0.0038 - accuracy: 1.0000"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-82c3bebf5076>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0minitial_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minitial_epochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_image_ds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     validation_steps=test_count//BATCH_SIZE)\n\u001b[0m",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    732\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 734\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    122\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 86\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    413\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 415\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    416\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m       \u001b[1;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1820\u001b[0m     \u001b[1;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1821\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1822\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1824\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1141\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1222\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m   1225\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    512\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "initial_epochs = 12\n",
    "fine_tune_epochs = 10\n",
    "total_epochs =  initial_epochs + fine_tune_epochs\n",
    "\n",
    "\n",
    "history = model.fit(\n",
    "    train_image_ds,\n",
    "    steps_per_epoch=train_count//BATCH_SIZE,\n",
    "    epochs=total_epochs,\n",
    "    initial_epoch = initial_epochs,\n",
    "    validation_data=test_image_ds,\n",
    "    validation_steps=test_count//BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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